Implementation of an optimal strategy for algorithmic debugging

[EN] One of the most automatic debugging techniques is Algorithmic Debugging because it allows us to debug a program without the need to inspect the source code. In order to find a bug, an algorithmic debugger asks questions to the programmer about the correctness of subcomputations in an execution. Reducing the number and complexity of these questions is an old objective in this field. Recently, an strategy for algorithmic debuggers that minimizes the number of questions has been released. This new strategy is called Optimal Divide and Query and, provided that all questions can be answered, it finds any bug in the source code with a minimum set of questions. In this work we discuss the implementation of such a strategy in different algorithmic debugging architectures. © 2011 Elsevier B.V. All rights reserved.This work has been partially supported by the Spanish Ministerio de Ciencia e Innovación (Secretar´ıa de Estado de Investigaci´on) under grant TIN2008-06622-C03-02 and by the Generalitat Valenciana under grant PROMETEO/2011/052Insa Cabrera, D.; Silva Galiana, JF. (2012). Implementation of an optimal strategy for algorithmic debugging. Electronic Notes in Theoretical Computer Science. 282:47-60. https://doi.org/10.1016/j.entcs.2011.12.005S476028

Instrumenting self-modifying code

Adding small code snippets at key points to existing code fragments is called instrumentation. It is an established technique to debug certain otherwise hard to solve faults, such as memory management issues and data races. Dynamic instrumentation can already be used to analyse code which is loaded or even generated at run time.With the advent of environments such as the Java Virtual Machine with optimizing Just-In-Time compilers, a new obstacle arises: self-modifying code. In order to instrument this kind of code correctly, one must be able to detect modifications and adapt the instrumentation code accordingly, preferably without incurring a high penalty speedwise. In this paper we propose an innovative technique that uses the hardware page protection mechanism of modern processors to detect such modifications. We also show how an instrumentor can adapt the instrumented version depending on the kind of modificiations as well as an experimental evaluation of said techniques.Comment: In M. Ronsse, K. De Bosschere (eds), proceedings of the Fifth International Workshop on Automated Debugging (AADEBUG 2003), September 2003, Ghent. cs.SE/030902

Optimization Techniques for Algorithmic Debugging

[EN] Nowadays, undetected programming bugs produce a waste of billions of dollars per year to private and public companies and institutions. In spite of this, no significant advances in the debugging area that help developers along the software development process have been achieved yet. In fact, the same debugging techniques that were used 20 years ago are still being used now. Along the time, some alternatives have appeared, but there still is a long way for them to be useful enough to get into the software development process. One of them is algorithmic debugging, which abstracts the information the user has to investigate to debug the program, allowing them to focus on what is happening instead of how it is happening. This abstraction comes at a price: the granularity level of the bugs that can be detected allows for isolating wrongly implemented functions, but which part of them contains the bug cannot be found out yet. This thesis focusses on improving algorithmic debugging in many aspects. Concretely, the main aims of this thesis are to reduce the time the user needs to detect a programming bug as well as to provide the user with more detailed information about where the bug is located. To achieve these goals, some techniques have been developed to start the debugging sessions as soon as possible, to reduce the number of questions the user is going to be asked about, and to augment the granularity level of those bugs that algorithmic debugging can detect, allowing the debugger in this way to keep looking for bugs even inside functions. As a result of this thesis, three completely new techniques have been defined, an already existent technique has been improved, and two new algorithmic debugging search strategies have been defined that improve the already existent ones. Besides these theoretical results, a fully functional algorithmic debugger has been implemented that contains and supports all these techniques and strategies. This debugger is written in Java, and it debugs Java code. The election of this language is justified because it is currently one of the most widely extended and used languages. Also because it contains an interesting combination of unsolved challenges for algorithmic debugging. To further increase its usability, the debugger has been later adapted as an Eclipse plugin, so it could be used by a wider number of users. These two debuggers are publicly available, so any interested person can access them and continue with the research if they wish so.[ES] Hoy en día, los errores no detectados de programación suponen un gasto de miles de millones al año para las empresas e instituciones públicas y privadas. A pesar de esto, no ha habido ningún avance significativo en el área de la depuración que ayude a los desarrolladores durante la fase de desarrollo de software. De hecho, las mismas técnicas de depuración que se utilizaban hace 20 años se siguen utilizando ahora. A lo largo del tiempo, han surgido algunas alternativas, pero todavía queda un largo camino para que estas sean lo suficientemente útiles como para abrirse camino en el proceso de desarrollo de software. Una de ellas es la depuración algorítmica, la cual abstrae la información que el programador debe investigar para depurar el programa, permitiéndole de este modo centrarse en el qué está ocurriendo en vez de en el cómo. Esta abstracción tiene un coste: el nivel de granularidad de los errores que pueden detectarse nos permite como máximo aislar funciones mal implementadas, pero no averiguar qué parte de estas contiene el error. Esta tesis se centra en mejorar la depuración algorítmica en muchos aspectos. Concretamente, los principales objetivos de esta tesis son reducir el tiempo que el usuario necesita para detectar un error de programación así como proporcionar información más detallada de dónde se encuentra el error. Para conseguir estos objetivos, se han desarrollado técnicas para iniciar las sesiones de depuración lo antes posible, reducir el número de preguntas que se le van a realizar al usuario, y aumentar el nivel de granularidad de los errores que la depuración algorítmica puede detectar, permitiendo así seguir buscando el error incluso dentro de las funciones. Como resultado de esta tesis, se han definido tres técnicas completamente nuevas, se ha mejorado una técnica ya existente, y se han definido dos nuevas estrategias de depuración algorítmica que mejoran las previamente existentes. Además de los resultados teóricos, también se ha desarrollado un depurador algorítmico completamente funcional que contiene y respalda todas estas técnicas y estrategias. Este depurador está escrito en Java y depura código Java. La elección de este lenguaje se justifica debido a que es uno de los lenguajes más ampliamente extendidos y usados actualmente. También debido a que contiene una combinación interesante de retos todavía sin resolver para la depuración algorítmica. Para aumentar todavía más su usabilidad, el depurador ha sido posteriormente adaptado como un plugin de Eclipse, de tal manera que pudiese ser usado por un número más amplio de usuarios. Estos dos depuradores están públicamente disponibles para que cualquier persona interesada pueda acceder a ellos y continuar con la investigación si así lo deseara.[CA] Hui en dia, els errors no detectats de programació suposen una despesa de milers de milions a l'any per a les empreses i institucions públiques i privades. Tot i això, no hi ha hagut cap avanç significatiu en l'àrea de la depuració que ajude als desenvolupadors durant la fase de desenvolupament de programari. De fet, les mateixes tècniques de depuració que s'utilitzaven fa 20 anys es continuen utilitzant ara. Al llarg del temps, han sorgit algunes alternatives, però encara queda un llarg camí perquè estes siguen prou útils com per a obrir-se camí en el procés de desenvolupament de programari. Una d'elles és la depuració algorítmica, la qual abstrau la informació que el programador ha d'investigar per a depurar el programa, permetent-li d'esta manera centrar-se en el què està ocorrent en compte de en el com. Esta abstracció té un cost: el nivell de granularitat dels errors que poden detectar-se ens permet com a màxim aïllar funcions mal implementades, però no esbrinar quina part d'estes conté l'error. Esta tesi es centra a millorar la depuració algorítmica en molts aspectes. Concretament, els principals objectius d'esta tesi són reduir el temps que l'usuari necessita per a detectar un error de programació així com proporcionar informació més detallada d'on es troba l'error. Per a aconseguir estos objectius, s'han desenvolupat tècniques per a iniciar les sessions de depuració com més prompte millor, reduir el nombre de preguntes que se li formularan a l'usuari, i augmentar el nivell de granularitat dels errors que la depuració algorítmica pot detectar, permetent així continuar buscant l'error inclús dins de les funcions. Com resultat d'esta tesi, s'han definit tres tècniques completament noves, s'ha millorat una tècnica ja existent, i s'han definit dos noves estratègies de depuració algorítmica que milloren les prèviament existents. A més dels resultats teòrics, també s'ha desenvolupat un depurador algorítmic completament funcional que conté i protegix totes estes tècniques i estratègies. Este depurador està escrit en Java i depura codi Java. L'elecció d'este llenguatge es justifica pel fet que és un dels llenguatges més àmpliament estesos i usats actualment. També pel fet que conté una combinació interessant de reptes encara sense resoldre per a la depuració algorítmica. Per a augmentar encara més la seua usabilitat, el depurador ha sigut posteriorment adaptat com un plugin d'Eclipse, de tal manera que poguera ser usat per un nombre més ampli d'usuaris. Estos dos depuradors estan públicament disponibles perquè qualsevol persona interessada puga accedir a ells i continuar amb la investigació si així ho desitjara.Insa Cabrera, D. (2016). Optimization Techniques for Algorithmic Debugging [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/68506TESISPremios Extraordinarios de tesis doctorale

Towards declarative diagnosis of constraint programs over finite domains

The paper proposes a theoretical approach of the debugging of constraint programs based on a notion of explanation tree. The proposed approach is an attempt to adapt algorithmic debugging to constraint programming. In this theoretical framework for domain reduction, explanations are proof trees explaining value removals. These proof trees are defined by inductive definitions which express the removals of values as consequences of other value removals. Explanations may be considered as the essence of constraint programming. They are a declarative view of the computation trace. The diagnosis consists in locating an error in an explanation rooted by a symptom.Comment: In M. Ronsse, K. De Bosschere (eds), proceedings of the Fifth International Workshop on Automated Debugging (AADEBUG 2003), September 2003, Ghent. cs.SE/030902

A Survey of Algorithmic Debugging

"© ACM, 2017. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in ACM Computing Surveys, {50, 4, 2017} https://dl.acm.org/doi/10.1145/3106740"[EN] Algorithmic debugging is a technique proposed in 1982 by E. Y. Shapiro in the context of logic programming. This survey shows how the initial ideas have been developed to become a widespread debugging schema ftting many diferent programming paradigms and with applications out of the program debugging feld. We describe the general framework and the main issues related to the implementations in diferent programming paradigms and discuss several proposed improvements and optimizations. We also review the main algorithmic debugger tools that have been implemented so far and compare their features. From this comparison, we elaborate a summary of desirable characteristics that should be considered when implementing future algorithmic debuggers.This work has been partially supported by the EU (FEDER) and the Spanish Ministerio de Economia y Competitividad under grant TIN2013-44742-C4-1-R, TIN2016-76843-C4-1-R, StrongSoft (TIN2012-39391-C04-04), and TRACES (TIN2015-67522-C3-3-R) by the Generalitat Valenciana under grant PROMETEO-II/2015/013 (SmartLogic) and by the Comunidad de Madrid project N-Greens Software-CM (S2013/ICE-2731).Caballero, R.; Riesco, A.; Silva, J. (2017). A Survey of Algorithmic Debugging. ACM Computing Surveys. 50(4):1-35. https://doi.org/10.1145/3106740S135504Abramson, D., Foster, I., Michalakes, J., & Sosič, R. (1996). Relative debugging. Communications of the ACM, 39(11), 69-77. doi:10.1145/240455.240475K. R. Apt H. A. Blair and A. Walker. 1988. Towards a theory of declarative knowledge. In Foundations of Deductive Databases and Logic Programming J. 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Declarative Debugging in Gödel. Ph.D. Dissertation. University of Bristol.B. Braßel and H. Siegel. 2008. Debugging Lazy Functional Programs by Asking the Oracle. Springer-Verlag Berlin 183--200. DOI:http://dx.doi.org/10.1007/978-3-540-85373-2_11 10.1007/978-3-540-85373-2_11 B. Braßel and H. Siegel. 2008. Debugging Lazy Functional Programs by Asking the Oracle. Springer-Verlag Berlin 183--200. DOI:http://dx.doi.org/10.1007/978-3-540-85373-2_11 10.1007/978-3-540-85373-2_11Caballero, R. (2005). A declarative debugger of incorrect answers for constraint functional-logic programs. Proceedings of the 2005 ACM SIGPLAN workshop on Curry and functional logic programming - WCFLP ’05. doi:10.1145/1085099.1085102Caballero, R., García-Ruiz, Y., & Sáenz-Pérez, F. (2012). Declarative Debugging of Wrong and Missing Answers for SQL Views. Lecture Notes in Computer Science, 73-87. doi:10.1007/978-3-642-29822-6_9Caballero, R., García-Ruiz, Y., & Sáenz-Pérez, F. (2015). 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Dagstuhl Reports : Volume 1, Issue 2, February 2011

Online Privacy: Towards Informational Self-Determination on the Internet (Dagstuhl Perspectives Workshop 11061) : Simone Fischer-Hübner, Chris Hoofnagle, Kai Rannenberg, Michael Waidner, Ioannis Krontiris and Michael Marhöfer Self-Repairing Programs (Dagstuhl Seminar 11062) : Mauro Pezzé, Martin C. Rinard, Westley Weimer and Andreas Zeller Theory and Applications of Graph Searching Problems (Dagstuhl Seminar 11071) : Fedor V. Fomin, Pierre Fraigniaud, Stephan Kreutzer and Dimitrios M. Thilikos Combinatorial and Algorithmic Aspects of Sequence Processing (Dagstuhl Seminar 11081) : Maxime Crochemore, Lila Kari, Mehryar Mohri and Dirk Nowotka Packing and Scheduling Algorithms for Information and Communication Services (Dagstuhl Seminar 11091) Klaus Jansen, Claire Mathieu, Hadas Shachnai and Neal E. Youn

DDC: un depurador declarativo para C++

Trabajo Fin de Máster en Métodos Formales en Ingeniería Informática, Facultad de Informática UCM, Departamento de Sistemas Informáticos y Computación, Curso 2021/2022.A declarative debugger for C++ is presented, called DDC. A declarative debugger receives as input an incorrect computation, builds a debugging tree based on the execution of the program and, after asking questions to an oracle (typically the user), points out a fragment of code that is the cause of the failure. We present the debugger’s main features, such as three different navigation strategies, using test cases as oracles, support for nonterminating programs and a tree transformation.Presentamos un depurador declarativo para C++, llamado DDC. Un depurador declarativo recibe como argumento de entrada una computación incorrecta, construye un árbol de depuración basado en la ejecución del programa y, después de preguntar a un oráculo (típicamente el usuario), indica el fragmento de código causante del fallo. Presentamos las principales características del depurador, tales como tres estrategias de navegación, el uso de casos de prueba como oráculo, capacidad de depurar programas que no terminan y una transformación de árbol.Depto. de Sistemas Informáticos y ComputaciónFac. de InformáticaTRUEunpu